Text Chunking Using Transformation-Based Learning
Lance Ramshaw,Mitchell Marcus +1 more
- pp 157-176
Reads0
Chats0
TLDR
This work has shown that the transformation-based learning approach can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive “baseNP” chunks.Abstract:
Transformation-based learning, a technique introduced by Eric Brill (1993b), has been shown to do part-of-speech tagging with fairly high accuracy. This same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive “baseNP” chunks. For this purpose, it is convenient to view chunking as a tagging problem by encoding the chunk structure in new tags attached to each word. In automatic tests using Treebank-derived data, this technique achieved recall and precision rates of roughly 93% for baseNP chunks (trained on 950K words) and 88% for somewhat more complex chunks that partition the sentence (trained on 200K words). Working in this new application and with larger template and training sets has also required some interesting adaptations to the transformation-based learning approach.read more
Citations
More filters
Proceedings ArticleDOI
Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding
TL;DR: In this paper, an attention-guided multi-layer multi-cross encoding scheme was proposed to address the challenges of not fully utilizing the unique characteristics and inherent relations of two different passages.
Journal ArticleDOI
Conditional Random Fields for Multiview Sequential Data Modeling
TL;DR: In this paper , a new multiview discriminant model based on conditional random fields (CRFs) is proposed to model multi-view sequential data, which inherits the advantages of CRFs that build a relationship between items in each sequence.
Journal Article
READ-BioMed@SocialDisNER: Adaptation of an Annotation System to Spanish Tweets
TL;DR: A system for named entity recognition for identifying biomedical concepts in English MEDLINE citations and Spanish clinical text for the LivingNER 2022 challenge was developed and minimal adaptation was required to perform namedentity recognition in the Spanish tweets in the SocialDisNER task.
Dissertation
Apprentissage automatique et compréhension dans le cadre d’un dialogue homme-machine téléphonique à initiative mixte
TL;DR: L’approche proposee dans cette these de conserver l’espace de recherche probabiliste tout au long du processus de comprehension en l�’enrichissant a chaque etape, yn â’n ôl nous menons des experiences sur le corpus MEDIA dans les memes conditions d’evaluation que lors of the campagne d”evaluation MEDIA.
Proceedings Article
Measuring Text Readability by Lexical Relations Retrieved from Wordnet
TL;DR: This study explores the relation between text readability and the conceptual categories proposed in Prototype Theory, and shows that a basic level word can be identified by its frequency to form compounds and the length difference from its hyponyms in average.
References
More filters
Book ChapterDOI
Parsing By Chunks
TL;DR: The typical chunk consists of a single content word surrounded by a constellation of function words, matching a fixed template, and the relationships between chunks are mediated more by lexical selection than by rigid templates.
Proceedings ArticleDOI
A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text
TL;DR: The authors used a linear-time dynamic programming algorithm to find an assignment of parts of speech to words that optimizes the product of (a) lexical probabilities (probability of observing part of speech i given word i) and (b) contextual probabilities (pb probability of observing n following partsof speech).
Proceedings Article
Some advances in transformation-based part of speech tagging
TL;DR: In this article, a rule-based approach to tagging unknown words is described, where the tagger-can be extended into a k-best tagger, where multiple tags can be assigned to words in some cases of uncertainty.
Journal ArticleDOI
Performance structures: A psycholinguistic and linguistic appraisal☆
James Paul Gee,François Grosjean +1 more
TL;DR: In this paper, two lines of research are combined to deal with a long-standing problem in both fields: why the performance structures of sentences (structures based on experimental data, such as pausing and parsing values) are not fully accountable for by linguistic theories of phrase structure.
Book
A corpus-based approach to language learning
TL;DR: A learning algorithm is described that takes a small structurally annotated corpus of text and a larger unannotated corpus as input, and automatically learns how to assign accurate structural descriptions to sentences not in the training corpus.